Imagine uttering a command, and an unseen voice answered your bidding. Previously only possible in fiction, AI voice assistants have long become a reality for many consumers. As you might be aware, Amazon Alex, Siri, and Google Alexa are the leading forces in the field.
With recent advancements in generative AI, we anticipate the possibilities of broader applications for AI voice assistants. For SMBs, it means growing opportunities that you want to capitalize on. After all, the voice recognition market, which AI voice assistant is a subset of, is predicted to hit US$15.87 by 2030. That’s more than twice the market volume in 2024.
The future of AI assistants is promising. But the question is, how do business owners build and incorporate the technology into their products? That's what we hope to answer in this guide.
We’re Oleh Komenchuk – a Machine arning Engineer at Uptech, and Andrii Bas – Uptech co-founder and AI expert. In this article, you’ll get:
- a comprehensive 10-step guide on how to make an AI voice assistant
- the list of features to include in your AI voice assistant
- and our expert tips on how to make the development efficient.
What are AI Voice Assistants and their types?
AI voice assistants are software that uses generative AI, machine learning, and natural language processing to interpret verbal commands and act on them. From setting an alarm to ordering deliveries, AI assistants can vary in technologies, purpose, and complexity.
Let’s explore the common ones.
Chatbots
They are apps that let you converse with AI through a chat interface but might also incorporate speech recognition technologies. For example, ChatGPT started as a generative AI chatbot but recently allowed users to interact verbally. Instead of typing their questions, users can speak directly to the chatbot and get a verbal response.
Voice assistants
Voice assistants are apps primarily designed to listen to users' commands, interpret them, and perform specific tasks. Alexa, Siri, and Google Assistant are examples of popular voice assistants that people use in daily life.
Specialized virtual assistants
Some AI voice assistants are designed for specific industries, which we call specialized virtual assistants. For example, a medical chatbot that converses with patients and schedules their appointments is a specialized virtual assistant.
AI Avatars
AI avatars put a face to the voice you hear from an AI chatbot. They are the animated form of characters or people that users can see when interacting through voice commands.
10 Steps to Create Your Own AI Voice Assistant from Scratch
Despite its simplicity, an AI voice assistant isn’t easy to build. There are layers of complexities beneath the simple chat interface that require highly specialized skill sets. Moreover, to create your own voice assistant, you need to consider security, compliance, scalability, adaptability, and more.
Below, we share the approach that our developers apply to build generative AI apps.
Step 1: Define your AI Assistant’s purpose
First, establish how the AI assistant adds value to the user experience. It’s important to determine if building or integrating a voice assistant clearly helps the user fulfill their goals. Otherwise, you might spend a hefty fee for an AI feature that serves very little purpose.
If you’re not sure whether users will find an AI assistant helpful, conduct a survey and interview them. Present your ideas, or better still, visualize how a voice assistant could help improve their app experience. When interviewing your target users, ask probing questions, such as:
- Can an AI voice assistant simplify the user’s interaction?
- Will it be helpful in solving their problems?
- How can the voice assistant integrate with existing capabilities?
Collect feedback, validate your ideas, and revise them. From there, you can determine features that you want to include in the AI voice assistant. We’ll explore this in depth later on as it’s an important topic.
Step 2: Choose the right technology stack
Next, determine the tech stack your AI voice assistant requires. Usually, this includes exploring a list of artificial intelligence and software technologies necessary to provide natural language understanding capabilities in your app.
Natural language processing libraries
Natural language processing (NLP) is one of the crucial pillars of AI voice assistants. When building such apps, you’ll need NLP libraries, which provide readily available frameworks that allow apps to interpret text more effortlessly. For example, Transformers library, spaCy, NLTK, and Hugging Face offer comprehensive NLP libraries that AI developers can leverage.
Machine learning libraries
Besides comprehending user commands, voice assistants need ways to process them intelligently. That’s where machine learning libraries like TensorFlow and PyTorch prove useful. Instead of building from scratch, AI developers can use and integrate pre-built AI functions with the app’s logic instead of building them from scratch.
Voice recognition and synthesis tools
Another critical tech stack that AI voice assistants can’t function without is voice recognition and synthesis frameworks. Voice recognition technologies enable the AI assistant to capture voice commands and turn them into a format that machine learning and NLP modules can process.
Meanwhile, audio synthesis tools turn the output into natural, audible voice responses for the users. Some frameworks that might help you with your development effort are CMU Sphinx and Google Text-to-Speech.
Programming languages
While AI frameworks are crucial, your developers need to bring them together. To do that, they write codes to integrate the app, backend services, and machine learning algorithms. We share popular programming languages that developers use for AI development.
- Python is arguably the most popular language in which AI developers program. It’s easy to learn, has a strong community, and provides developers access to an expensive library of frameworks.
- Java, on the other hand, is known for its scalability and flexibility. You can use Java to build cross-platform apps or add AI features to an existing software infrastructure.
- C++ is helpful if you want to build an AI virtual assistant that requires more control of the device's native capabilities and performance. Developers use C++ for low-level programming, especially in robotics, games, and computer vision applications,
We know that determining which programming language to use for a specific AI app is not easy. Therefore, we share how they compare to each other in the table below.
Step 3: Collect and prepare data
Before you build an AI voice assistant, you need to train the underlying AI model. To do that, developers must prepare the appropriate training data. For the model to develop voice processing capabilities, you need two types of data: audio and text.
- Audio data allows the virtual assistant to process and interpret different languages, speaking styles, and accents.
- Textual data lets you train the language model to understand the contextual relationship and meaning of various commands.
It’s important to recognize that the quality of training data directly influences the voice assistant’s performance. Knowing what type of data to collect is crucial. Usually, when creating a voice assistant, we collect voice recordings from various speakers.
We want to create a fairly distributed training data sample that realistically represents how people speak in real life. So, some of the recordings might contain background noise, which the AI model must learn to filter out.
After collecting the speech samples, you’ll need to transcribe them into text and label them. That’s because we train AI voice assistants through supervised learning. During training, the machine learning algorithm attempts to match the voice to the task it should perform through the annotations.
Let’s say you want the AI voice assistant to open the Gmail app when hearing “Open Gmail”. You’ll need to specify the context in the labels so the AI algorithm can associate the command with the output.
Step 4: Preprocess and clean data
We want to stress again that if you train your AI assistant with low-quality data, you’ll get subpar performance. That’s why it’s crucial to put the data you collected through two stages: data preprocessing and data cleaning.
Data preprocessing
Humans speak differently than the way they write. Often, verbal speech isn’t optimized for machine learning algorithms. Our day-to-day conversations may contain filler words, repetition, silence gaps, and other irrelevant artifacts that might hamper model learning.
Therefore, data scientists preprocess the data by removing unnecessary audio parts. They also perform tasks like tokenization and stop-word removal to ensure the training data contains only the audio parts the model needs.
Data cleaning
Excessive noise or background chatter can prevent developers from training the model. Therefore, developers might need to filter background noise or other audio elements to ensure optimized model training.
Tips from Uptech: Throughout the entire data preparation, be mindful of data security and ethical considerations.
- Ensure the audio data is diverse enough to fairly represent your user’s demographics.
- Secure data collected and prepared to train AI models.
- Comply with relevant data privacy acts such as HIPAA and GDPR, your business operates in highly regulated industries like finance, medical, and properties.
Step 5: Train your AI assistant
The next phase in creating a personal AI assistant is developing its speech recognition capabilities. Typically, you’ll need to put the AI model through these processes.
Model training
Train your AI assistant so it can learn to understand the speech and business context it’s meant to serve. This involves feeding the AI model with volumes of training samples you’ve prepared.
During the training, the speech recognition model analyzes and learns linguistic patterns, which it can recall later. Once trained, the speech model can converse naturally with users in language and tone that humans understand.
The problem with training a model is the immense computing resources required. You’ll need to invest in special AI hardware, such as GPU and TPU, to train and deploy the AI models. On top of that, it might take weeks or months before you can adequately train a model.
Fine tuning
At Uptech, we don’t recommend training an entire speech recognition model from scratch. That’s because the process is costly and takes significant time that SMB owners can’t afford. Rather, we recommend fine-tuning pre-trained models from AI providers like OpenAI.
When you fine-tune a model, you allow the model to learn new information while retaining its existing capabilities. For SMBs, fine-tuning is the better approach in terms of time and cost. For example, you can use Whisper, a multilingual pre-trained speech recognition model trained on over 680K of audio data.
Then, fine-tune the model with commands and information related to your business. This way, you can quickly get the AI assistant to the market without spending an astronomical amount of money on development.
Model evaluation
Can you confidently deploy an AI model you’ve trained? Rather than relying on guesswork, evaluate the trained model. Compare the model’s result with industrial benchmarks to ensure the model is accurate, consistent, and responsive when responding to user commands.
We have a comprehensive guide where we explain how to build AI software from scratch and dive deep into how to prepare the data, train it, and tune it. Check it out!
Step 6: Design the UI/UX
If you want your AI-powered voice assistant to meet user expectations, having an eloquent chatbot isn’t enough. Instead, you’ll need to pay attention to the UI/UX elements of your app so that it engages users throughout their interaction.
Remember, users want to solve their problems seamlessly with your app. That means mapping out their journey from the moment they sign up for the app. Proper choice of colors, layouts, fonts, and visual elements helps to a certain extent. However, you should also focus on conversational flow.
When you create an AI voice assistant, you must anticipate various scenarios that users might encounter. For example, they might ask questions to which the AI model cannot satisfactorily respond. In such cases, decide what the model should do, such as courteously presenting other options to the user.
To incorporate conversational design, follow these tips:
- Identify the common queries that users ask and map the conversation for them.
- Avoid jargon or overly technical language. Instead, use common words that laypersons understand.
- Ensure that the voice assistant responds naturally instead of adopting a rigid or robotic style.
Here’s our expert article on how to build a conversational AI. Check it out for more insights!
Step 7: Develop or integrate
As your AI experts train the voice assistant, your development team can simultaneously work on the user-facing app. Usually, this involves creating an independent AI voice assistant app or integrating the capability into an existing one.
Develop an independent AI app
If you create an AI assistant app from scratch, you’ll need to go through the entire software development lifecycle. And that includes discovery, planning, resource allocation, development, and testing. You need to consider not only the AI features but also the app's business logic.
Many SMBs might struggle to focus on developing AI voice assistants and the apps that use them at the same time. If possible, we recommend the alternate approach below.
Integrate with an existing system
Instead of building a new app, you can integrate the voice assistant into an existing one. For example, you already have an eCommerce app that users love, but you want to add a voice assistant that helps them shop better. To do that, you can train the AI voice assistant and use APIs to integrate it with the existing app.
Of course, AI integration is only possible if your existing app can be easily modified to include the necessary APIs. Otherwise, you’ll have no choice but to rebuild the entire app. Either way, both approaches can take up resources that few SMBs can afford. That’s why many business owners outsource AI development to a reliable partner like Uptech.
Step 8: Test and debug
Don’t deploy your app until you’ve satisfactorily tested it for bugs, security, and other issues. Deep learning models and speech recognition technologies are still improving and may occasionally produce unhelpful responses. Besides, users might be concerned about data security and privacy when you introduce a new feature, especially AI.
So, make app testing a priority and not an afterthought. At Uptech, we continuously test the AI engine and app throughout the development. To ensure the final product is stable, accurate, and consistent, our QA engineers run several types of tests, including:
- Unit tests, which ensure all individual software modules are developed according to specifications.
- Integration tests check for compatibility issues when multiple services are combined together.
- Security tests, where we perform static and dynamic analysis to uncover vulnerability risks in the codes, libraries, and modules the app uses.
- System tests provide a holistic view of the app.
Step 9: Deploy and support
Once you’re satisfied with the QA reports, prepare to deploy the app. Depending on the type you’ve built, you’ll need to upload it to AppStore or Playstore. Also, your developers will need to update the database and backend services you host in the cloud infrastructure.
Now, don’t rest on your laurels after launching the app. Be prepared for any incidents, such as latent bugs or usability issues, that might arise in the subsequent days. Get your support team on standby so they can immediately respond to user requests.
Step 10: Make improvements
Like any product, you must monitor user feedback, trends, and technological shifts so you can improve the AI assistant accordingly. For example, users may want to perform more tasks with voice commands, which requires building or revising certain app functions.
While you continuously optimize the app to remain relevant, pay attention to data security, compliance, and other ethical concerns.
Develop a Custom Solution or Use an AI Assistant Builder?
When it comes to creating a voice assistant, you have two options — build a custom one or use an AI assistant builder.
A custom solution, as the name implies, allows you to create a voice assistant that fully meets your business requirements. To create a custom AI voice assistant, developers must build, integrate, and test every module the app requires.
Meanwhile, an AI assistant builder provides more flexibility. Instead of building from scratch, you can use the readily-templates to accelerate development.
But how do you decide which option to choose, and what are the implications when you choose one over the other?
When to build a custom solution?
If your business requires a highly customized app and a budget to spare, a custom solution is better. Generally, custom AI voice assistants are more scalable and can be tailored to fit exact project requirements. You will have full control over data management, privacy, and workflow if you choose this option. However, you will pay a higher cost and face a longer time-to-market if you build a custom AI voice assistant.
When to use an AI builder
Many SMBs want to get a prototype up quickly and test it in the market. In that case, it’s better to choose an AI builder. With prebuilt templates and a drag-and-drop interface, you can set up and streamline an AI voice assistant with your business quickie. Plus, it’s less expensive than building a custom app. The drawback? You’re limited to the technologies the AI builder offers, which might limit scalability and customization.
How to build an AI assistant for free
If you want to quickly get an AI voice assistant for free, try VoiceFlow and SiteSpeakAI. They are a popular voice chatbot builder that many SMBs use in their organizations. VoiceFlow lets you train a voice assistant with business knowledge bases and deploy in channels like Whatsapp, Discord, and Slack. Meanwhile, SiteSpeakAI is a builder for creating an AI-powered support agent that can converse verbally with users.
Check out how both AI chatbot builders compare below.
What Features Should You Include in Your Voice Assistants?
These features are essential to create a purposeful AI voice assistant.
- Automatic speech recognition. It allows the app to accurately interpret spoken words and transcribe them to textual form.
- Natural language understanding enables the AI assistant to understand contextual relationships and converse naturally in various scenarios.
- Task automation links specific commands to actions that users want to accomplish.
- Personalization lets every user customize their preferences, including style, commands, and responses.
- Information sources. The voice assistant can retrieve information to provide real-time updates or relevant domain-specific data.
5 most popular AI voice assistants
To better understand how these features apply in actual use cases, let’s explore some of the most popular AI voice assistants.
Siri
Siri is an intelligent virtual assistant exclusively designed for Apple users. It applies machine learning, natural language understanding, and other AI technologies to converse intelligently and perform in-device tasks.
Alexa
Alexa is Amazon’s flagship AI-powered virtual assistant that responds to various commands. As part of a broader ecosystem, it can automate smart home systems, retrieve information from various sources, help customers shop on Amazon, and more.
Cortana
Microsoft Cortana works closely with Bing to help users query the internet, schedule reminders, and more with verbal commands. Once prevalent in various Microsoft Products, it’s now being succeeded by CoPilot, a GPT-powered AI Chatbot.
Google Assistant
Google Assistant is an AI virtual assistant that runs on Android devices. Like Siri, you can use Google Assistant to set alarms, check the weather forecast, browse the web, and more by simply interacting with it verbally.
Bixby
Bixby is Samsung’s AI voice assistant offering, and it is now available on most of the brand’s smart devices. By tapping into machine learning and voice recognition technologies, Bixby can interpret requests, fulfill them, and learn from the interactions.
Why Creating An AI Voice Assistant Is Worth It
No doubt, you’ll pay more if you create your own AI voice assistant. However, the benefits of doing so can make it a worthwhile investment for SMBs. We share some of the common perks if you choose to build one.
1. Personalization
You can personalize how the AI assistant interacts with app users. Because you’re not constrained by a third-party provider’s limitations, you can train the machine learning model to learn the user’s preference and converse in styles they’re comfortable with.
For example, if you’re integrating a voice assistant into a medical app, it’s better to build one that takes the patient’s sensitivity into consideration.
2. Increased efficiency
An AI voice assistant helps you automate mundane tasks more effortlessly. Let’s say you want to set a reminder. Instead of opening an app and jotting it down, you can command the voice assistant to do so without disrupting your workflow.
3. Data privacy
If you use an AI builder, you’re relinquishing control of data management to a third party. This, unfortunately, might breach compliance regulations in certain industries. Conversely, building your own AI voice assistant puts you in control of how the data are collected, stored, and secured.
4. Creativity
If your business is driven by creativity, building your own AI voice assistant will fuel your team further. Without being boxed by third-party constraints, they can freely ideate, experiment, and challenge existing conventions. Often, such freedom leads to breakthroughs and new products.
5. Scalability
A custom-built AI voice assistant is more scalable than an out-of-the-box chatbot from a third party. For the former, you can scale it to meet growing user demands. Meanwhile, you cannot expand, update, or customize a third-party voice assistant freely.
6. Innovation
You can be an early adopter of emerging technologies when you build your own AI voice assistant. Rather than waiting for updates from the chatbot builder, your developers can freely innovate to provide a competitive edge.
7. Integration
There is no restriction to third-party integration with your own voice assistant. You can introduce voice chat to other products you use as part of your strategic plan.
For example, if your AI voice chatbot proves successful in customer support, you can introduce it for work scheduling, inventory management, and other operational tasks.
Uptech Tips for AI Voice Assistant Development
Over the years, we’ve helped business owners worldwide develop or integrate AI assistants with their products. Consider these tips to avoid potential complications and accelerate your development.
Implement sentiment analysis and natural language understanding
Both technologies are the core of a successful AI-based voice assistant. Sentiment analysis allows the app to interpret the underlying sentiment when a user expresses a verbal command. Meanwhile, natural language understanding is crucial to comprehend the contextual relationship of continuous conversations.
Utilize APIs for voice and command recognition
Without voice recognition, your AI assistant can only understand text-based commands. So, you’ll need to integrate a speech-to-text engineer into your app. To do that, we recommend using APIs. This way, you don’t require excessive coding or building the entire functionality from scratch. Instead, you can leverage pre-built functionalities from third-party speech recognition engineers to save time and resources.
Anonymize user data and regularly conduct security audits
Don’t neglect data security when you’re creating the AI assistant. Because it involves storing and processing volumes of data, the is a heightened risk of data leakage or cyber threats. Continuously assess your data security posture and apply protective measures such as encryption, anonymization, and application security tests.
Summary
More people want the convenience of verbally conversing with a chatbot to get their tasks done. As generative AI continues to evolve, SMBs are in a stronger position to offer such services, whether internally or to end users. But to start, most business owners face the dilemma of building their own AI voice assistant or using a builder.
We’ve presented the pros and cons of both approaches. Ultimately, if you want better control over security, innovation, and scalability, it’s better to create your own voice chatbot. If you’re worried about the technicalities involved, you can outsource the development works to a reliable partner.
Uptech has completed hundreds of projects, including generative AI-powered apps like Tired Banker, Angler AI, and Dyvo AI.
Contact our team to build your AI voice assistant with us.
FAQ
How do voice assistants work?
They listen to verbal commands from users. Then, they transcribe them into textual form and feed it to a language model, such as ChatGPT, which has been trained on domain-specific data. After understanding the context, intent, and command, the voice assistant would automate designated tasks and provide a response.
Can an AI Assistant be integrated with existing software systems?
It depends on whether your existing software can support the APIs and other requirements the AI assistant requires. If the software is built on a legacy framework, it likely needs a major revamp or a total rebuild. Otherwise, you can integrate an AI assistant with an app with APIs.
How long does it take to build an AI Assistant?
If you build a simple AI assistant capable of answering basic questions, it’ll take 2-3 months. But expect longer development time your app requires complex NLP, voice recognition, and system integrations. Some AI assistants that offer multi-lingual support and extensive customization might take more than 12 months to build.
How much does it cost to develop an Al assistant app like Alexa?
Alexa is a complex AI assistant that goes beyond basic question-answering. It allows users to control connected devices, access information from various sources, and more. From designing the app's UI/UX interface to training the underlying machine learning algorithm, you’ll need a team of multiskilled developers, AI experts, QA engineers, and business analysts.
We estimate the cost of building such apps to be $150,000 or more.